Abstract
The coordinated control of vehicle actuators is gaining more and more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Furthermore, wheel individual motors allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimisation of performance and energy consumption issues. In this paper, the problem of minimum-time manoeuvring in EVs is addressed, and the proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.

Abstract
Fault detection has been an open problem in power converters for several years. In this paper, we aim at detecting faults by means of left invertibility techniques. Using a mathematical model of the power converter it is possible to exploit the principle of injectivity of I/O map, which allows the recovery of unknown inputs applied to the system from the measure of the outputs. To achieve this end, we utilize the existing current and voltage sensors of the converter without the need for any additional sensors. Finally, a case-study of a multiport converter is presented and simulated to illustrate the methodology. Experimental results obtained under realistic conditions illustrate the effectiveness of the scheme and prove that fault detection based on the inverse method is possible.

Abstract
During many years, battery models have been proposed with different levels of accuracy and complexity. In some cases, simple low-order aggregated battery pack models may be more appropriate and feasible than complex physic-chemical or high-order multi-cell battery pack models. For example: in early stages of the system design process, in non-focused battery applications, or whenever low configuration effort or low computational complexity is a requirement. The latter may be the case of Electrical Equivalent Circuit Models (EECM) suitable for energy optimization purposes at a system level in the context of energy management or sizing problem of energy storage systems. In this paper, an improved parametrization method for Li-ion linear static EECMs based on the so called concept of direct current resistance (DCR) is presented. By drawing on a DCR-based parametrization, the influence of both diffusion polarization effects and changing of Open-Circuit Voltage (OCV) are virtually excluded on the estimation of the battery's inner resistance. This results in a parametrization that only accounts for pure ohmic and charge transfer effects, which may be beneficial, since these effects dominate the battery dynamic power response in the range of interest of many applications, including electro-mobility. Model validation and performance evaluation is achieved in simulations by comparison with other low and high order EECM battery models over a dynamic driving profile. Significant improvements in terms of terminal voltage and power losses estimation may be achieved by a DCR-based parametrization, which in its simplest form may only require one short pulse characterization test within a relatively wide range of SoCs and currents. Experimental data from a 53 Ah Li-ion pouch cell produced by Kokam (Type SLPB 120216216) with Nickel Manganese Cobalt oxide (NMC) cathode material is used.

Abstract
This paper is concerned with the study of combined sizing and energy management algorithms for electric vehicles (EVs) endowed with batteries and supercapacitors (SCs). The main goal is to find the number of cells of each source that minimizes the installation and running costs of the EV, taking into account the performance requirements specified for the vehicle and the technical constraints of the energy sources. To tackle this problem, two methodologies will be investigated. The first considers a filter-based approach to perform the power split among the sources; it will be shown that, under some practical assumptions, the resultant sizing problem can be posed as a linear programming problem and solved using efficient numerical techniques. The second methodology employs an optimal noncausal energy management, which, when integrated with the sizing problem, yields a nonlinear optimization problem. These two methodologies will be then applied to size the storage unit of a small EV. The results indicate that the filter-based approach, although simple and numerically efficient, generally requires an oversized storage unit. Furthermore, it was also concluded that, if the range requirements of the EV are not very high (below 50 km, in our case study), the use of SCs enables energy savings of up to 7.8%.

Abstract
This paper presents a comparative study between two non-causal algorithms for the energy management problem of electric vehicles, endowed with batteries and supercapacitors(SCs). Toward that goal, an optimization-based energy problem is formulated, which targets the minimization of the source's energy losses throughout a given driving cycle. This problem is solved, firstly, with the help of a fast (but locally optimal) non-linear programming solver; and, secondly, with a slow, but globally optimal, dynamic programming (DP) approach. Simulation results will demonstrate that, despite the different theoretical properties associated with these two solver approaches, both generate similar solutions. In the second part of the work, we will develop a filter-based energy management algorithm, i.e., employ batteries to provide the low-frequency content of the power demand, while SCs cover the high-frequency demand. Our approach builds on the idea of adapting the filter's time constant throughout the vehicle's journey, using, for that purpose, a fuzzy logic algorithm and the information of the state of the vehicle. In comparison with the traditional fixed time-constant approach, the simulation results show that under some conditions the adaptive time-constant algorithm has the potential to reduce the energy losses of the sources by up to 62%. © 2013 IEEE.

Abstract
Contemporary safety standards prescribe processes in which system safety requirements, captured early and expressed in the form of Safety Integrity Levels (SILs), are iteratively allocated to architectural elements. Different SILs reflect different requirements stringencies and consequently different development costs. Therefore, the allocation of safety requirements is not a simple problem of applying an allocation "algebra" as treated by most standards; it is a complex optimisation problem, one of finding a strategy that minimises cost whilst meeting safety requirements. One difficulty is the lack of a commonly agreed heuristic for how costs increase between SILs. In this paper, we define this important problem; then we take the example of an automotive system and using an automated approach show that different cost heuristics lead to different optimal SIL allocations. Without automation it would have been impossible to explore the vast space of allocations and to discuss the subtleties involved in this problem.